We utilize the CoNLL-2003 dataset [1]} for POS Tagging and NER tasks. The CoNLL-2003 dataset consists of news articles from the Reuters
RCV1 corpus with POS and NER tags. We formulate POS Tagging and NER as token-level classification tasks following standard practice. Specifically, POS Tagging has 47 classes and NER has 9 classes. We take the token embedding of the last hidden layer of BERT [2]} as the input to a linear layer, which is then used as the classifier over the POS/NER label set. The token ID is set as the input \(x\)  for the hash function \(g(\cdot )\) . F1 score is hired for the evaluation metric.
